“How do we do that?” An analysis of TikToks by lesbians over age 30 representing sexual identity, lived experience over time, and solidarity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lesbians have long turned to digital media and technologies for information, support, and to self-represent sexual identity in ways that have the capacity for building communities and gathering publics and counterpublics. TikTok is a short video platform popular with young people, which has increasingly seen the participation of comparatively older users. This paper investigates the self-representation of lesbians over age 30 on TikTok to understand the themes in their content and how the platform shapes their communication with others. Through sampling tailored to TikTok's algorithmic curation, ten lesbians' accounts are examined alongside qualitative coding and analysis of 50 of these creators' videos. Findings reveal key themes regarding the expression of identity and age, lived experience over time, and bids for connection and community. TikTokers expressed lesbian identity in continuity with longstanding stereotypes to enhance visibility but also incorporated humor and youthful trends to give rise to novel identity expressions. Videos showcasing the passage of time and sociopolitical change demonstrated the resilience of lesbian lives and conveyed hope while advice and statements of solidarity expressed support for young people's present struggles with homophobia and transphobia. Contrasting with studies of TikTok's generational wars, this article shows how older lesbians are building generational bridges through their uptake of youth-driven platform practices, sharing of past challenges to support youth in overcoming present hurdles, and by modeling lesbian futures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it